PERAMALAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN METODE BACKPROPAGATION DAN RECURRENT NEURAL NETWORK (RNN) - GITALOKA, TJOKORDA I. A. PUTRI; KENCANA, I PUTU EKA N.; HARINI, LUH PUTU IDA (E-Jurnal Matematika, 2024)
Forecasting is an objective calculation and uses past data to predict something in the future. The economic activity of buying and selling shares is a common activity in this digital era. Investment in the capital market is currently popular among the general public. Forecasting in the digitalization era has undergone many developments, as well as its application not only in agriculture, but also in economics, politics and other fields. The development of forecasting in the economic field is growing rapidly with the use of more complex mathematical models and the application of information technology. Forecasting is an objective calculation and uses past data to predict something in the future. Utilization of artificial neural network (ANN) is one of the solutions for forecasting with more accurate results. One of the algorithms in ANN is the backpropagation algorithm. Apart from using other backpropagation forecasting methods that can be used is the deep learning (DL) method with a recurrent neural network algorithm. The backpropagation method produces an optimal MAPE of 13.28%. with an architectural model of seven input layer neurons, seven hidden layer neurons, and using the sigmoid activation function. The recurrent neural network method produces an optimal MAPE of 1.12%. with an architectural model of seven input layer neurons, ten hidden layer neurons, and using the tanh activation function. If the two optimal MAPEs of each method are compared, the Recurrent Neural Network Method produces more optimal forecasting compared to the backpropagation method.